Der Forscher, der eine komplexe Struktur verstehen will, ist gut beraten, wenn er sich in die Rolle eines solchen Demiurgen versetzt und sich vorstellt, er hätte sie selbst konstruieren müssen. Natürlich muss er dafür eine begründete Vermutung haben, was sie leisten soll. Leistung schließt immer eine Zielvorgabe ein, die Arbeit des Demiurgen läuft also naturgemäß im Rahmen einer telischen Heuristik ¹ ab.

N. Bischof (in prep, S. 143)

The researcher who wants to understand a complex structure is well advised to put himself in the role of such a demiurge and imagine that he would have had to construct it himself. Of course, to do this he must have an educated guess as to what it should perform. Performance always includes a target, so the demiurge’s work naturally runs within the framework of a telic heuristic.

Scenario: The environment has a source of food and a source of water, in a substantial distance from each other. The donkey has a general metabolism that continuously consumes food and water reserves in the body; this consumption is higher during physical activity. As soon as one of the two reserves in the body drops to zero, the donkey dies.

(Simplifying) assumptions:

• The donkey has a fixed velocity.
• Linear decrease in both water and food reserves in the body, higher under activity.
• No other needs or tasks (no predators etc., no sleep necessary).
• Eating & drinking takes a substantial amount of time (1 unit/time step).
• The donkey has a representation of the environment and knows where the two sources are.

Task: Construct an organism, with as few assumptions as possible, that survives as long as possible. Which constructs / sensors / abilities are necessary for this?

→ Assume realistic capabilities, which respect the evolutionary path dependency.

E.g., don’t assume …

• Mammal organisms that need 3 hands to perform a task
• Omniscient knowledge about the environment, probabilities, distant events, the future, …
• Unlimited cognitive processing capabilities (“In the first 2ms, the simulated amoeba performs a multidimensional Bayesian optimization task”)

• Consider the underlying psychological research question: How can an organism solve an approach-approach conflict?
• Sketch your intial model in the style of Bischof. Refine where necessary, e.g.:
  • Think about which sensors are necessary (both towards internal states and towards the environment) and which actors are necessary
  • Are the inputs to sensors cues that are realistically observable by a simple organism?
  • Give a label to every variable (i.e., every arrow).
• Draw the model collaboratively in draw.io
  • In a corner, add the version number 0.1 and today’s date (simply as a text box)
• Export the model as an xml file and push it to your group’s Github repo
  • Write a meaningful commit message, e.g. Initial commit; model version 0.1

In the lecture, you learned how a good construct definition looks like. Apply that new skill to your model!

• Develop proper definitions for all variables and concepts in your model
• If necessary, udpate the model structure in draw.io, export a new xml file.
• Provide the definitions of the model’s variables and constructs in a separate markdown file.
• Push both files to your group’s Github repo.
  • Write a meaningful commit message that explains the changes.
  • Create a CHANGELOG.md file that explains the changes in the model in more detail.

Define the variables in the formal model: Possible range/values (define the set); prior probabilities (justify! empirical evidence?); interpretability (what does a value of 0.7 mean?) Funktionskurven definieren

• Implement the model in Netlogo
• Push the .nlogo to your group’s Github repo.
  • Write a meaningful commit message that explains the changes.
  • Create a CHANGELOG.md file that explains the changes in the model in more detail.